Field of the Invention
The invention relates to the field of medical image processing, and more particularly to a method for reconstructing a Positron emission tomography (PET) image using graphics processing unit (GPU) parallel computing.
Description of the Related Art
PET image is usually reconstructed by using an analytical method, an iterative method, and a state-space based method.
A typical analytical method is a filtered back-projection method (FBP). It uses the Fourier transform to obtain the original data from a projected data. However, this model doesn't follow the real situation of PET imaging. Furthermore, FBP method cannot suppress noise, which causes a low quality result.
A typical iterative method adopts a maximum likelihood-expectation maximum (ML-EM) algorithm and a maximum a posteriori (MAP) algorithm. The ML-EM algorithm estimates the intensity of each voxel using the maximum likelihood estimation method. In the MAP algorithm, the objective function is the posterior distribution of voxels' intensity. Each voxel's intensity value is obtained by finding the optimal solution of the posterior. Iterative methods rely on certain statistical model. However, there are a lot of factors that influence the quality of PET imaging, including physiological information, structural information and so on. Such information cannot be used in the iterative methods, which is a drawback of these methods.
A typical state-space based method is adapted to model the process of PET imaging and to merge statistical information, physiological information and structural information into the reconstruction algorithm. The quality of reconstructed image is much better than those of iterative methods and analytical methods. The existing algorithm of solving state-space model is Kalman filtering method. This method assumes that the distribution of observed data is Gaussian, while the distribution of PET data is Poisson. The quality of Kalman filtering's solution is not very high due to this mismatch.
Because of the large data size and complex reconstruction algorithms, a long time is required to reconstruct an image, which is unsuitable for clinical usage. The traditional CPU computing is very slow due to its serial computing. On the other hand, high parallel CPU computing requires a very expensive supercomputer, which is not always practical in commercial usage.